Data Quality Guide

A comprehensive guide to understanding and improving data quality, from foundational concepts to advanced monitoring and metrics.

Why you should care about data quality

Learn to implement systematic data quality frameworks to transform your organization, ensuring reliability in data-driven decisions and processes.

What is data quality?

Explore the eight dimensions of data quality and why they serve as the leading indicators of organizational trust in data

Data governance and data quality

Dive into the essentials of data governance and data quality, exploring their importance, differences, and best practices for implementing effective governance strategies in the tech world.

What's the difference between data observability and data quality?

Learn about the primary differences between data observability and data quality methodologies and use cases.

Datafold's origin story

Learn why Datafold co-founder, Gleb Mezhanskiy, decided to make a proactive data quality testing tool

What is proactive data quality testing?

Learn the three principles of proactive data quality for modern data engineering: shifting tests left in the workflow, implementing a data checklist before shipping, and automating data quality checks.

Enhancing data quality: Lessons from FINN, Petal, and Rocket Money

Explore how FINN, Petal, and Rocket Money improved data integrity and stakeholder satisfaction by adopting shift-left and proactive data quality testing in their data operations.

Introduction

Understand where along data workflows is testing data paramount to high data quality.

During data transformation development & deployment

High data quality in dbt transformations is crucial for preventing issues in production and enhancing development speed. This post covers common challenges in ensuring data quality and scaling dbt projects effectively.

During data migrations

Discover the three data quality challenges of data migrations: validating system parity, managing resource conflicts, and achieving stakeholder sign-off, with strategies for effective resolution.

During ongoing data replication

Understand key data quality challenges in data replication such as inadequate testing, custom solution failures, and ETL outages, impacting data accuracy and reliability.

Nurturing a data quality mindset

Discover key strategies to build a data-driven culture that supports data quality, from automating validation tasks to fostering data literacy among stakeholders.

You need data quality metrics, but which ones?

Learn the drawbacks of using aggregates and the importance of value-level based metrics in effectively identifying and resolving data quality issues.

How to improve data quality: Practical strategies

Explore how to improve data quality with practical strategies like shift-left testing, CI/CD pipeline integration, and column-level lineage to ensure accurate, reliable data.

Data quality monitoring: When and where to automate

Explore effective strategies for data quality monitoring to maintain the accuracy and reliability of your analytics, driving informed business decisions.

A practical roadmap for creating a robust data quality system

Explore the building blocks and hierarchy of a mature data quality strategy.

The work continues: Iterating on your data quality initiatives

Like all data work, creating a long-term and scalable solution for high quality data takes time, effort, and evolution.